AI for diagnosis and treatment
The use of machine learning in the medical field is one of the most ambitious applications of this technology. Cesare Furlanello, data science expert at the Bruno Kessler Foundation of Trento, introduced us to this last frontier of research.
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Cesare Furlanello received his degree in Mathematics from the University of Padua, Italy, in 1986. He has worked at the Bruno Kessler Foundation (Center for Scientific and Technological Research of Trento) since 1987 where he is now a senior researcher and head of the MPBA project ( formerly ITC-IRST Neural Networks for Complex Data Analysis Project)
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Machine learning is the new technological challenge that develops machines with the ability to learn on their own without having been explicitly programmed.
There are many applications made possible by this technique, such as autonomous driving, facial recognition and automatic translation; the use of machine learning in the medical field is one of the latest and most ambitious applications of this technology. How can new technologies help develop research in the pharmacological field and improve clinical practice?
Cesare Furlanello, head of the MBPA unit (Predictive Models for Biomedicine and Environment) at the Bruno Kessler Foundation of Trento, a data science expert, introduced us to the last frontier of research: machine learning applied to medicine. "In FBK we have been dealing with applied machine learning for twenty years and the improvement of health has always been the focus of our projects" - explained Dr. Furlanello - "Doctors are used to take decisions, weighing risks and creating similarities: therefore this is an ideal environment for developing systems that make predictions about the course of the disease, or on the positive or not positive individual response to drugs.
The step to personalised medicine is short: the goal is to give the right drug to the right patient at the most appropriate time, avoiding useless drug intake or adverse reactions due to the individual characteristics or the coexistence of other drugs or other environmental effects. An advantage is that digital health data are constantly increasing: this availability makes it possible to adopt pervasive machine learning techniques and actually methods are becoming necessary for overcoming the limits of manual analysis".
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